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Similar articles for PubMed (Select 23173094)

1.

Effectiveness of genomic prediction of maize hybrid performance in different breeding populations and environments.

Windhausen VS, Atlin GN, Hickey JM, Crossa J, Jannink JL, Sorrells ME, Raman B, Cairns JE, Tarekegne A, Semagn K, Beyene Y, Grudloyma P, Technow F, Riedelsheimer C, Melchinger AE.

G3 (Bethesda). 2012 Nov;2(11):1427-36. doi: 10.1534/g3.112.003699. Epub 2012 Nov 1.

2.

Genome properties and prospects of genomic prediction of hybrid performance in a breeding program of maize.

Technow F, Schrag TA, Schipprack W, Bauer E, Simianer H, Melchinger AE.

Genetics. 2014 Aug;197(4):1343-55. doi: 10.1534/genetics.114.165860. Epub 2014 May 21.

3.

Genome-based prediction of testcross values in maize.

Albrecht T, Wimmer V, Auinger HJ, Erbe M, Knaak C, Ouzunova M, Simianer H, Schön CC.

Theor Appl Genet. 2011 Jul;123(2):339-50. doi: 10.1007/s00122-011-1587-7. Epub 2011 Apr 20.

PMID:
21505832
4.

Genomic prediction of hybrid performance in maize with models incorporating dominance and population specific marker effects.

Technow F, Riedelsheimer C, Schrag TA, Melchinger AE.

Theor Appl Genet. 2012 Oct;125(6):1181-94. doi: 10.1007/s00122-012-1905-8. Epub 2012 Jun 26.

PMID:
22733443
5.

Usefulness of multiparental populations of maize (Zea mays L.) for genome-based prediction.

Lehermeier C, Krämer N, Bauer E, Bauland C, Camisan C, Campo L, Flament P, Melchinger AE, Menz M, Meyer N, Moreau L, Moreno-González J, Ouzunova M, Pausch H, Ranc N, Schipprack W, Schönleben M, Walter H, Charcosset A, Schön CC.

Genetics. 2014 Sep;198(1):3-16. doi: 10.1534/genetics.114.161943.

6.

Genomic prediction in biparental tropical maize populations in water-stressed and well-watered environments using low-density and GBS SNPs.

Zhang X, Pérez-Rodríguez P, Semagn K, Beyene Y, Babu R, López-Cruz MA, San Vicente F, Olsen M, Buckler E, Jannink JL, Prasanna BM, Crossa J.

Heredity (Edinb). 2015 Mar;114(3):291-9. doi: 10.1038/hdy.2014.99. Epub 2014 Nov 19.

PMID:
25407079
7.

Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines.

Riedelsheimer C, Technow F, Melchinger AE.

BMC Genomics. 2012 Sep 4;13:452. doi: 10.1186/1471-2164-13-452.

8.

Correlations and comparisons of quantitative trait loci with family per se and testcross performance for grain yield and related traits in maize.

Peng B, Li Y, Wang Y, Liu C, Liu Z, Zhang Y, Tan W, Wang D, Shi Y, Sun B, Song Y, Wang T, Li Y.

Theor Appl Genet. 2013 Mar;126(3):773-89. doi: 10.1007/s00122-012-2017-1. Epub 2012 Nov 27.

PMID:
23183923
9.

Prediction of single-cross hybrid performance in maize using haplotype blocks associated with QTL for grain yield.

Schrag TA, Maurer HP, Melchinger AE, Piepho HP, Peleman J, Frisch M.

Theor Appl Genet. 2007 May;114(8):1345-55. Epub 2007 Feb 24.

PMID:
17323040
10.

Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

Crossa J, Campos Gde L, Pérez P, Gianola D, Burgueño J, Araus JL, Makumbi D, Singh RP, Dreisigacker S, Yan J, Arief V, Banziger M, Braun HJ.

Genetics. 2010 Oct;186(2):713-24. doi: 10.1534/genetics.110.118521. Epub 2010 Sep 2.

11.

Genomic predictability of interconnected biparental maize populations.

Riedelsheimer C, Endelman JB, Stange M, Sorrells ME, Jannink JL, Melchinger AE.

Genetics. 2013 Jun;194(2):493-503. doi: 10.1534/genetics.113.150227. Epub 2013 Mar 27.

12.

Impact of selective genotyping in the training population on accuracy and bias of genomic selection.

Zhao Y, Gowda M, Longin FH, Würschum T, Ranc N, Reif JC.

Theor Appl Genet. 2012 Aug;125(4):707-13. doi: 10.1007/s00122-012-1862-2. Epub 2012 Apr 6.

PMID:
22481121
13.

Genomic prediction in CIMMYT maize and wheat breeding programs.

Crossa J, Pérez P, Hickey J, Burgueño J, Ornella L, Cerón-Rojas J, Zhang X, Dreisigacker S, Babu R, Li Y, Bonnett D, Mathews K.

Heredity (Edinb). 2014 Jan;112(1):48-60. doi: 10.1038/hdy.2013.16. Epub 2013 Apr 10.

14.

Performance prediction of F1 hybrids between recombinant inbred lines derived from two elite maize inbred lines.

Guo T, Li H, Yan J, Tang J, Li J, Zhang Z, Zhang L, Wang J.

Theor Appl Genet. 2013 Jan;126(1):189-201. doi: 10.1007/s00122-012-1973-9. Epub 2012 Sep 13.

PMID:
22972201
15.

Accuracy of genomic selection in European maize elite breeding populations.

Zhao Y, Gowda M, Liu W, Würschum T, Maurer HP, Longin FH, Ranc N, Reif JC.

Theor Appl Genet. 2012 Mar;124(4):769-76. doi: 10.1007/s00122-011-1745-y. Epub 2011 Nov 11.

PMID:
22075809
16.
17.

Prediction of single-cross hybrid performance for grain yield and grain dry matter content in maize using AFLP markers associated with QTL.

Schrag TA, Melchinger AE, Sørensen AP, Frisch M.

Theor Appl Genet. 2006 Oct;113(6):1037-47. Epub 2006 Aug 3.

PMID:
16896712
18.

Genomewide predictions from maize single-cross data.

Massman JM, Gordillo A, Lorenzana RE, Bernardo R.

Theor Appl Genet. 2013 Jan;126(1):13-22. doi: 10.1007/s00122-012-1955-y. Epub 2012 Aug 11.

PMID:
22886355
19.

The impact of population structure on genomic prediction in stratified populations.

Guo Z, Tucker DM, Basten CJ, Gandhi H, Ersoz E, Guo B, Xu Z, Wang D, Gay G.

Theor Appl Genet. 2014 Mar;127(3):749-62. doi: 10.1007/s00122-013-2255-x. Epub 2014 Jan 24.

PMID:
24452438
20.

Molecular marker-based prediction of hybrid performance in maize using unbalanced data from multiple experiments with factorial crosses.

Schrag TA, Möhring J, Maurer HP, Dhillon BS, Melchinger AE, Piepho HP, Sørensen AP, Frisch M.

Theor Appl Genet. 2009 Feb;118(4):741-51. doi: 10.1007/s00122-008-0934-9. Epub 2008 Dec 2.

PMID:
19048224
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